Quantile Regression: Applications on Experimental and Cross Section Data using EViews

Quantile Regression is a quantitative research method used to model and analyze the relationships between a dependent variable and one or more independent variables. It estimates the conditional quantile regression (QR) using mean-regression equations, which can quickly present the statistical results of a set of nine QR(τ)s for τ(tau)s from 0.1 up to 0.9. This book provides examples of statistical results of various QR analyses based on experimental and cross-section data of various regression models, covering one-way, two-way, and n-way ANOVA quantile regressions, QRs with multi numerical predictors, heterogeneous QRs, and latent variables QRs. It also offers expert guidance on forecasting and drawing conclusions from the results.

Format: Hardback
Length: 496 pages
Publication date: 08 July 2021
Publisher: John Wiley and Sons Ltd

Quantile Regression: A Comprehensive Presentation to Unlock Richer Insights from Data Analyses

Quantile Regression, a powerful quantitative research method, models and analyzes the relationships between a dependent variable and one or more independent variables. It employs the conditional least-square or mean-regression (MR) analysis, where each equation estimation of a regression yields a single regression function or fitted values variable. As an advanced mean regression analysis, each estimation equation can directly estimate the conditional quantile regression (QR), enabling rapid presentation of statistical results for a set of nine QR(τ)s, ranging from 0.1 to 0.9, to predict the detailed distribution of the response or criterion variable. QR holds significant importance across various disciplines, including statistics, econometrics, ecology, healthcare, and engineering.

Quantile Regression: Applications on Experimental and Cross Section Data Using EViews offers practical examples of statistical outcomes derived from diverse QR analyses utilizing experimental and cross-sectional data from various regression models. The author delves into the applications of one-way, two-way, and n-way ANOVA quantile regressions, QR with multi-numerical predictors, heterogeneous QRs, and latent variables QRs, among others. Throughout the text, readers gain valuable insights into developing optimal quantile regressions and conducting advanced analysis techniques such as the quantile process, Wald test, redundant variables test, residual analysis, stability test, and omitted variables test. This comprehensive volume:

• Describes how QR offers a more detailed perspective on the relationships between independent variables and the quantiles of the criterion.
• Emphasizes the significance of QR in uncovering insights that traditional regression methods may overlook.
• Provides a thorough exploration of the theoretical foundations and practical applications of quantile regression.
• Serves as a valuable resource for researchers, practitioners, and students seeking to leverage the power of quantile regression in their data analyses.

Weight: 1030g
Dimension: 177 x 250 x 35 (mm)
ISBN-13: 9781119715177